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| import torch | |
| from transformers import AutoModel, AutoTokenizer | |
| import gradio as gr | |
| import soundfile as sf | |
| import numpy as np | |
| import tempfile | |
| # Load model and tokenizer | |
| device = "cpu" # or "cuda" if available | |
| model = AutoModel.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True).to(device) | |
| tokenizer = AutoTokenizer.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True) | |
| # Mapping: language -> speaker_id | |
| LANG_SPEAKER_MAP = { | |
| "asm": 0, "ben": 2, "brx": 4, "doi": 6, | |
| "kan": 8, "mai": 10, "mal": 11, | |
| "mar": 13, "nep": 14, "pan": 16, | |
| "san": 17, "tam": 18, "tel": 19, | |
| "hin": 13 # use Marathi Male voice for Hindi (close) | |
| } | |
| # Mapping: Style (fixed default) | |
| DEFAULT_STYLE_ID = 0 # ALEXA | |
| def tts_from_json(json_input): | |
| try: | |
| text = json_input["text"] | |
| lang = json_input["language"].lower() | |
| speaker_id = LANG_SPEAKER_MAP.get(lang) | |
| if speaker_id is None: | |
| return f"Language '{lang}' not supported." | |
| inputs = tokenizer(text=text, return_tensors="pt").to(device) | |
| outputs = model(inputs['input_ids'], speaker_id=speaker_id, emotion_id=DEFAULT_STYLE_ID) | |
| waveform = outputs.waveform.squeeze().cpu().numpy() | |
| sample_rate = model.config.sampling_rate | |
| # Save to temp file for Gradio playback | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: | |
| sf.write(f.name, waveform, sample_rate) | |
| return sample_rate, waveform | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| iface = gr.Interface( | |
| fn=tts_from_json, | |
| inputs=gr.JSON(label="Input JSON: {'text': '...', 'language': 'mar/hin/san'}"), | |
| outputs=gr.Audio(label="Generated Audio"), | |
| title="VITS TTS for Indian Languages (Marathi, Hindi, Sanskrit)", | |
| description="Uses ai4bharat/vits_rasa_13. Supports Marathi, Hindi, and Sanskrit." | |
| ) | |
| iface.launch() | |